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16.05.2020 | Methodologies and Application

Uncertain vector autoregressive model with imprecise observations

verfasst von: Han Tang

Erschienen in: Soft Computing | Ausgabe 22/2020

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Abstract

Prior uncertain autoregressive (UAR) model research has focused principally on a univariate time series. However, different variables tend to influence each other in reality. In order to fill this gap, this paper explores the interrelationships among different variables and proposes an exposition of uncertain vector autoregressive (UVAR) model. Furthermore, we choose the least squares principle to estimate the unknown parameters in the UVAR model and analyze the residual of disturbance term. Then, we present the point estimation and confidence interval of the variables in the next period. Finally, the empirical results show that essential improvements in forecasting can be obtained by adding relative variables.

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Titel: Uncertain vector autoregressive model with imprecise observations
verfasst von: Han Tang
Publikationsdatum: 16.05.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 22/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-04991-9

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